Billy Yang expanded the HELM benchmark in the stanford-crfm/helm repository by developing a VoxCeleb2-based audio identification scenario. He implemented the VoxCeleb2Scenario, which processes audio clips and compares speakers to enable realistic speaker recognition evaluation within the benchmark. His work included creating new run configurations, scenario logic, and reusable audio manipulation utilities, all integrated using Python and YAML. By incorporating the VoxCeleb2 dataset, Billy enhanced HELM’s ability to assess model performance on audio identification tasks. The engineering focused on dataset integration and audio processing, resulting in deeper benchmarking capabilities without introducing major bug fixes during the development period.